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#940
Oct 22, 2018 · 1 hr
Google JavaScript with Malte Ubl
Google Search is a highly interactive JavaScript application. As you enter a query, results are being automatically suggested to you before you even finish typing. When you press enter, some of your search results may be widgets that represent the weather, the price of a stock, a recipe for green bean soup, or a language translation for a phrase. These complex frontend components are loading dynamically–the Google Search application cannot prefetch every single possible widget that you might ask for–but the results do load very quickly. Google has many other examples of advanced JavaScript engineering. The company is mostly known for backend engineering inventions like MapReduce, TensorFlow, Dremel, and Spanner. To turn these backend tools into user facing products, Google develops its own JavaScript frameworks and infrastructure to deliver information from the backend to the frontend. “Backend” and “frontend” are not precise terms. At Google, there are so many layers of infrastructure between a user and the data center. If you are an engineer working on a service at Google, you probably have several “frontends” and “backends” on either side of you. Malte Ubl is a senior staff engineer at Google. He’s heavily involved in Google’s JavaScript infrastructure, and has written about managing large JavaScript applications in detail. He also works on AMP, an open-source project for delivering web pages in a fast, performant format. He joins the show to describe Google’s history with JavaScript frameworks, the process of building frontends and middleware to deliver JavaScript applications, and the engineering behind AMP. There are criticisms of AMP, but some of them misunderstand how the AMP technology actually works. AMP allows pages to be cached, prefetched, and served to a user more quickly. AMP does not necessarily centralize pages around being served from Google Search. A good example of AMP speeding up pages outside of Google is reddit. We recently launched a new podcast: Fintech Daily! Fintech Daily is about payments, cryptocurrencies, trading, and the intersection between finance and technology. You can find it on fintechdaily.co or Apple and Google podcasts. We are looking for other hosts who want to participate. If you are interested in becoming a host, send us an email: host@fintechdaily.co
#939
Oct 19, 2018 · 48 min
AWS Containers with Deepak Singh
Deepak Singh is the director of compute services at AWS, where he works on cloud products relating to containers, Linux, and High Performance Computing. In today’s show, Deepak describes how the market for containers and serverless has evolved, and how Amazon thinks about product strategy. Back in 2014, Docker containers were becoming a popular way to deploy and manage application infrastructure. Containers allowed people to take advantage of their servers in a more economical way. Containers let developers move faster by quickly setting up and tearing down small composable units of software. As these containers grew in number within software companies, companies started figuring out that they needed tooling to manage and orchestrate all these containers. Infrastructure software companies realized that there would be a big business in providing orchestration software to developers who needed to manage these high volumes of containers. This led to the “container orchestration wars”, in which a variety of companies such as Red Hat, CoreOS, Docker, and Mesosphere all began to offer platforms for managing containerized applications. During the container orchestration wars, many large enterprises such as banks and telcos resisted picking any specific container orchestration system because there was no clear winner. Enterprises were hesitant to place a large bet on an infrastructure orchestration tool that might go out of fashion. Amazon had a large number of customers that wanted to orchestrate their containers, but it was unclear how the market for open source container orchestration was going to unfold. Around this time, Amazon created ECS, a closed-source container orchestration system. In the following years, Kubernetes was released and became the most popular container orchestrator. Amazon released EKS, a managed Kubernetes service. They also released AWS Lambda for running serverless functions and AWS Fargate for spinning up long-lived container instances. Deepak and I discuss the history of containers at Amazon, but we also discuss how developer preferences are changing towards managed services, and how AWS is able to continually build off of its own tools to build higher and higher level services for developers.
#937
Oct 18, 2018 · 59 min
Open Source Product Strategy with Shaun Connolly
Linux has created much more value for Google than it has for Linus Torvalds. Ruby on Rails has created more value for Airbnb than it has for David Heinemeier Hansson. Successful open source projects create more value than their creators’ capture–and that’s one reason why collections of people on the Internet are often inspired to work together on open source. When an engineer creates an open source project, and that open source project finds a large audience, that engineer can often build a successful business. SpringSource, Cloudera, and Elastic are examples of massively successful enterprises that were founded by the creators of open source software. But in other cases, the value of an open source project gets largely captured by cloud providers that create a closed source version of the open source project and offer it as a service. Shaun Connolly has worked in senior strategic roles at software companies such as SpringSource, VMWare, and Hortonworks. Throughout his decades of experience, much of his time has been spent figuring out how to monetize open source projects intelligently. Shaun joins the show to talk about his past experiences building enterprises, as well as modern issues–such as how to compete with major cloud providers. We also discuss the Commons Clause license, a new software license that open source projects can use to try to protect their value from being entirely captured by a cloud provider. Software Engineering Daily is looking for sponsors. If you are interested in reaching over 50,000 developers, you can go to softwareengineeringdaily.com/sponsor to find out more, and you can send us a message. We’d love to hear from you. And if you are an engineer working at a company that is marketing to developers, or hiring developers, if you tell your marketing department or your recruiting department about softwareengineeringdaily.com/sponsor, that is one way to help us out.
#936
Oct 17, 2018 · 47 min
Cloud Search with Liam Cavanagh
Search is part of almost every application. Users search for movies to watch. Engineers search through terabytes of log messages to find exceptions. Drivers search through maps to find a destination. Search remains an unsolved problem, with lots of room for optimization. Many search applications have been built Elasticsearch, an open source distributed search engine. Elasticsearch is the code that powers some search-as-a-service products offered by major cloud providers. After eight years of open source development, Elasticsearch is excellent at core search functionalities, such as indexing data, sharding, and serving queries. With improved access to machine learning tools, search applications can advance in new and interesting ways. For example, an incoming search query can be sent to an API for natural language processing before being served by the search engine. A natural language processing API can derive additional meaning from the query, adding metadata to a search query. Machine learning can also be applied to better understand how people are searching across your search index, and to optimize the search index to incorporate those user preferences. Liam Cavanagh is the principal program manager on Azure Search. He joins the show to talk about the architecture of a search index, how search queries are served by an index, and how machine learning APIs can be used to improve queries.
#934
Oct 16, 2018 · 38 min
Open vSwitch: Virtual Networking with Ben Pfaff
Virtual machines are operating system instances that run alongside each other on the same physical host. The virtual machines running on a physical host are managed by a hypervisor running on the physical host. A cluster of two physical servers could have four virtual machines running across those two physical instances. Those four virtual machines can communicate over a virtual switch. A network switch allows packets of bytes to be routed between machines. With a physical network switch, a dedicated physical device sits in the computer network to do this routing. A virtual network switch provides this packet routing without needing a dedicated physical hardware device for routing. Open vSwitch is a distributed virtual multilayer switch. Open vSwitch provides network switching for hardware virtualization environments. Ben Pfaff is a core contributor to Open vSwitch, and he joins the show to talk about operating system virtualization. Ben was an early employee at Nicira, a company that made significant developments in software-defined networking before being acquired by VMware in 2012.
#933
Oct 15, 2018 · 58 min
Data Warehouse with Christian Kleinerman
A data warehouse provides fast access to large data sets for analytics, data science, and dashboards. A data warehouse differs from a transactional database, because you often do not need to update specific records. Because of the read-only nature of the access patterns, and the high volumes of data being queried, the design of a data warehouse is very different than a transactional database. With a transactional database (such as MySQL or MongoDB), it is important to have consistency guarantees. For example, consider a transactional database that serves as the backend for banking applications. If multiple frontend servers are hitting that transactional database to withdraw money, you need the records to be quickly updated. You need to avoid race conditions, so that two servers cannot withdraw the entire bank account balance simultaneously from different locations. In contrast to transactional databases, a data warehouse is often used to process a query that encompasses a big data set. For example, Netflix might want to answer the question: “how many users that watched House of Cards also watched Black Mirror?” Netflix has a lot of users, so they will want to be accessing those user records in a way that lets them scan through the records quickly. Christian Kleinerman is the VP of product at Snowflake Computing. Snowflake’s main product is a cloud data warehouse. In today’s show, we talk about the difference between a data warehouse, a data lake, and a transactional database, and the process of moving data sets between them, often known as ETL. This show continues our series on data engineering and data platforms. As companies accumulate more and more data, the complexity of managing that data and taking full advantage of it is escalating. Christian gives his perspective on these changing trends, and describes the plans for Snowflake to evolve as a business.
#932
Oct 12, 2018 · 1 hr 1 min
DevOps at Microsoft with Martin Woodward
The Windows operating system is one of the most widely used pieces of software in history. Windows was started before there was any alternative to a monolithic codebase, because Microsoft was building software before the Internet was widely used by consumers. Networked computers gave rise to web applications, and software engineers began to rethink how to build everything. Software development got reimagined with agile. Monolithic codebases got broken up into service-oriented architecture. Instead of going to a store to buy a box with software in it, users downloaded software from the Internet, and that software was regularly updated. Software that is regularly updated needs to be regularly tested. Instead of a single round of testing for every round of boxed software that was shipped to a store, continuous testing and delivery gradually became the norm. The process of releasing and operating software became its own set of engineering challenges–which was tackled by the “operations” or “sys admin” team at a software company. Now there were two different sets of engineers–those who were developing the software and those who were operating the software. The incentives of these two types of engineers were not completely aligned. The software developers wanted to build software quickly and release new features. The operators wanted things to release software slowly, because if something broke then the operators were the first line of defense for fixing it. These problems between development and operations gave rise to the “DevOps” movement, in which developers and operations started working more closely together and sharing responsibilities. Incentives became aligned, and new types of software was created to facilitate more harmonious relationships between developers and operations–for example, continuous delivery pipelines. Today, most enterprises are still undergoing a transformation from monolithic software release cycles to continuous delivery. This is often referred to as a “DevOps Transformation”. A DevOps Transformation requires the entire organization to reorient itself around faster software release cycles. This can be a painful process, and we have covered it in many past shows. Hearing case studies from enterprises can be helpful for figuring out how to reorient your own enterprise. Microsoft is a useful case study in shifting towards DevOps. Windows is perhaps the biggest monolithic codebase in history. The fact that Microsoft could rearchitect Windows to be easier to work with should provide some reassurance to other enterprises who are currently undergoing their own migrations. Martin Woodward has been at Microsoft for 13 years and he joins the show to talk about how software delivery within the company has evolved. We discussed the move from boxed software delivery to delivery via the cloud, and focused on a few specific, longstanding products such as Windows. Martin has been part of the effort to build Azure DevOps, which is a product that offers similar tools to the ones Microsoft built internally for DevOps as a service. We also talk about the specific difficulties that enterprises often have when moving toward DevOps. Full disclosure: Microsoft is a sponsor of Software Engineering Daily.
#931
Oct 11, 2018 · 1 hr 3 min
Generative Models with Doug Eck
Google Brain is an engineering team focused on deep learning research and applications. One growing area of interest within Google Brain is that of generative models. A generative model uses neural networks and a large data set to create new data similar to the ones that the network has seen before. One approach to making use of generative models is GANs: generative adversarial networks. GANs can use a generative model (which creates new examples) together with a discriminator model (which can classify examples). As an example, let’s take the task of generating new pictures of cats. We want an artificial cat picture generator. First, we train a discriminator by feeding it billions of example pictures of cats. We now have a model that can tell what a cat is. Next, we make a model that generates completely random images. We feed those randomly generated images to the discriminator. The discriminator outputs a “loss” for these random images. Loss is a metric we can use to represent how far off a given image is from being something that the discriminator would recognize as a cat. Finally, you can feed this “loss” back into the generative model, so that the generative model will adjust its weights in a way that will reduce loss. Over time, the generator gets better and better at reducing loss, until the discriminator starts believing that some of these semi-random images are actually cats. Generative model systems have produced useful applications, such as object detection, image editing, and text-to-image generation. Today’s guest Doug Eck works on the Magenta team at Google Brain. Magenta uses applications of deep learning to produce tools and experiments around music, art, and creativity. In a previous show, Doug described his vision for humans and computers to work together on creative tasks such as music. Today, we dive into some of the core machine learning building blocks that make machine creativity possible.
#930
Oct 10, 2018 · 1 hr 3 min
Mapillary: Computer Vision Crowdsourcing with Peter Neubauer
Mapillary is a platform for gathering photos taken by smartphones and using that data to build a 3D model of the world. Mapillary’s model of the world includes labeled objects such as traffic signs, trees, humans, and buildings. This 3D model can be explored much like you can explore Google Street view. The data set that underlies Mapillary is crowdsourced from volunteer users who are taking pictures from different vantage points. These smartphone photos are uploaded to Mapillary, queued, and processed to constantly update and refine the Mapillary model. Mapillary processes high volumes of photos from around the world. The images in these photos need to be correctly fit into Mapillary’s model of the world like a puzzle piece sliding into place. The image needs to be segmented into the different entities within, and those entities need to be put through object recognition algorithm. When two pictures have a conflict, that conflict needs to be resolved. Mapillary is full of interesting engineering problems. The high volume of images and the level of processing has created the need for a unique sequence of indexing, queueing, and distributed processing using Apache Storm. In addition to processing all of this data and building a 3-D model, Mapillary serves an API for querying geolocations about traffic signs, road conditions, and bus stops. Peter Neubauer is the co-founder of Mapillary, and is also a co-founder of Neo Technology, the company behind Neo4j. Peter is a world-class engineer and he joins the show to give a detailed overview of the technology behind Mapillary, from ingressing the photos to running data engineering jobs to serving the API.
#928
Oct 9, 2018 · 57 min
Digital Privacy with Aran Khanna
When Aran Khanna was a college student, he accepted an internship to work at Facebook. Even before his internship started, he started playing around with Facebook’s APIs and applications. Aran built a Chrome extension called Marauder’s Map, which used Facebook Messenger’s web APIs to track where people lived, what their schedule was, and other highly sensitive information. These were not public features of Messenger, but Aran was able to reverse engineer the APIs. As a result, of making Marauder’s Map, Aran’s invitation to work at Facebook was retracted. Aran remained curious about the norms of publicly available social network data, and the second order data sets that could be built on top. Out of this curiosity, Aran created a tool called Money Trail, which used public Venmo data to model a graph of how users were paying each other. Aran showed for a second time that data that seems innocent to share can be repurposed to identify, classify, and incriminate users. Developers of these online applications face tradeoffs between privacy, convenience, and security. By interacting with these applications, we generate data that suggests how we think, what we like to do, and who we are affiliating with. Google and Facebook probably understand you better than you understand yourself. Aran Khanna previously was on the show to talk about machine learning at the edge. At the time he worked at Amazon Web Services. He now works as a digital privacy researcher. His background in machine learning makes him well-equipped to talk through the subtleties of modern digital privacy. In this show, Aran returns to talk through the finer points of privacy, data, and artificial intelligence.
#926
Oct 8, 2018 · 47 min
Airbnb Engineering with Surabhi Gupta
Airbnb began in 2008 as a monolithic Rails application serving the simple purpose of listing homes for rental. Over time, the number of listings increased dramatically, as did the number of people who were renting. With that scale, the Rails app had to be broken into different services, and entire teams were built out to focus on challenges such as pricing, application infrastructure, and search. Surabhi Gupta joined in 2013 to work on the search team, and has worked on different teams at Airbnb over time. Today she is a director of engineering leading the Homes business for Airbnb, which includes Growth, Search, Hosts, Pricing, and Business Travel. Surabhi has helped scale Airbnb through a hypergrowth period, and joins the show to share those experiences. One distinct area that we spent time on was Airbnb’s search engine. Surabhi formerly worked at Google, and she described how the engineering problem of a search engine for homes differs from a general purpose search engine like Google.
#925
Oct 6, 2018 · 53 min
Monolith Migration with Jan Schiffman and Sherman Wood
We previously released this episode with the wrong audio file and are re-releasing it on a weekend. TIBCO was started in the 90’s with a popular message bus product that was widely used by finance companies, logistics providers, and other systems with high throughput. As TIBCO grew in popularity, the company expanded into other areas through products it developed in-house as well as through acquisitions. One acquisition was Jaspersoft, a business intelligence data platform. When TIBCO acquired Jaspersoft in 2014, the architecture was a monolithic Java application. Around this time, customer use cases were shifting from centralized reporting to real-time, embedded visualizations. The use case of the Jaspersoft software was becoming less centralized and less monolithic and the software architecture needed to change in order to reflect that. Jan Schiffman is a VP of engineering at TIBCO and Sherman Wood is a director at TIBCO. They join the show to discuss the process of migrating a large Java monolith to a composable set of services. Breaking up a monolith is not an easy process–nor is it something that every company should do just because they have a monolith. In some cases, a monolith is just fine. Jan and Sherman explain why the business use case for why the Jaspersoft monolith needed to be refactored, and their approach to the refactoring. We also talk through the modern use cases of embedded analytics and the interaction between business analysts and data engineers. At a higher level, we discuss the lessons they have learned from managing a large, complex refactoring. Full disclosure: TIBCO is a sponsor of Software Engineering Daily.
#923
Oct 5, 2018 · 53 min
Scalyr: Column-Oriented Log Management with Steve Newman
Log messages are fast, high volume, unstructured data. Logs are often the source of metrics, alerts, and dashboards, so these critical systems are downstream from a log management system. A log management system needs to be highly available, so that a failure in one part of your system will not be correlated with failure of the log management system. Users of a log management system are often building tools based off of the query engine of that log management system. For example, I might build a dashboard that gives me a line graph representing the number of times a certain log message is alerting me due to a memory warning. I write a query to return the instances of these memory warnings, and my line graph is a visual representation that query. A log management system needs to be able to quickly serve users that are querying their logs–whether for dashboards or for ad-hoc queries. When logs are ingested by a log management system, the logs get parsed in a way that can bring some structure to the blob of text that is a raw log message. Some log management systems will then add the log message to an index. An index can allow for very fast lookups of particular types of queries. But an index also has certain constraints–such as processing regular expression queries. Steve Newman is the CEO and founder of Scalyr, a log management system that uses a column-oriented data storage system instead of the more conventional index-based log management systems. Today’s episode is a great case study in distributed systems tradeoffs. Steve talks in great detail about how Scalyr maintains high uptime, and its system for ingesting logs and serving queries.
#921
Oct 4, 2018 · 1 hr 2 min
Database Performance and Optimization with Andrew Davidson
When a database gets large, it can start to perform poorly. This can manifest in slow query speed. You can speed up a query by defining an index, which is a data structure that allows for faster access to the data that is being indexed. As a consequence, whenever you update the database, you will now need to update the index with that new piece of data. The more you index your data, the faster the access time. In order to have more indexes you must pay a write penalty in order to maintain consistency around that data, since the indexes need to be updated with each new entry. This illustrates one simple tradeoff that a developer can make within a database deployment. Why are there so many different databases in the world? Why do we need SQL databases like Postgres, document databases like MongoDB, key/value systems like Cassandra, and search systems like Elasticsearch? Because each of these each system optimizes for different sets of tradeoffs. Tradeoffs can affect the speed of a read, the speed of a write, the user experience, the consistency of data, and the cost of running the database. Andrew Davidson is the lead product manager of MongoDB Atlas. Andrew joins the show to talk about how database performance can degrade when a database gets large, and how to measure and optimize performance of a critical database. Andrew explores the range of distributed systems cases–from a single node database to a multi-geographic distribution of nodes around the world, and describes how the configuration of a database in the cloud can help or hurt the application that the database is serving. Full disclosure: MongoDB is a sponsor of Software Engineering Daily.
#919
Oct 3, 2018 · 49 min
Cursor: Data Collaboration with Adam Weinstein
Linkedin is an organization with thousands of employees. An enterprise of that size starts to develop problems with data collaboration. Data collaboration is the process of sharing and analyzing data with multiple users, such as data scientists, business analysts, and engineers. How do data scientists know what questions to ask? How do business analysts know the right way to query a database? How does a data engineer even find where the right database is within the company infrastructure? And how can these different users share information with each other so that redundant work is avoided? When Adam Weinstein was at Linkedin, he saw these problems firsthand. The process of accessing and utilizing data felt slow and broken. Engineers were searching through a company wiki to find out how to leverage data, and the wiki was often out of date. When an engineer would leave the company, there was not a durable, institutional memory of how that engineer worked with data. Adam used this experience as inspiration for Cursor, a tool for data collaboration. Cursor allows different users in the data pipeline to share data sets, queries, access patterns, and comments about data within a company. Cursor is used by Linkedin, Slack, Apple, and other companies. Adam is the CEO of Cursor, and he joins the show for an interview about the problems and opportunities of data collaboration.
#917
Oct 2, 2018 · 44 min
Kotlin Design with Andrey Breslav
Kotlin is a statically typed programming language that started as a JVM language. It gained popularity because it reduces the amount of boilerplate code required for a typical Java project. Many of the early adopters of Kotlin were building Android apps or Java applications, but it has grown to a variety of use cases including at companies like Uber, Pinterest, and Atlassian. Andrey Breslav is the lead language designer of Kotlin at JetBrains. He joins the show to describe the original goals of Kotlin, the compilation path of the language, and how it has moved beyond its days of only running on the JVM.
#915
Oct 1, 2018 · 52 min
Continuous Integration in Open Source with Oren Novotny
Open source software is key to our software infrastructure. Closed source enterprises rely on open source software, but the development processes for closed source and open source software are often different in their approach to continuous integration and delivery. Oren Novotny is a chief architect of DevOps and modern software at BlueMetal Architects where he works with a variety of clients to build products and internal applications. Oren spends lots of time developing open source software for his job as well as during his spare time. He’s been in the software industry for more than 15 years, and has a wide breadth of insights from different businesses in how they apply software. We started the conversation talking about electronic trading companies, which in some ways operate like large enterprises and in other ways operate like startups. Oren described working in the financial industry through the 2008 crisis, then switching industries to work at Microsoft, before coming to BlueMetal Architects. We then discussed the process of setting up continuous integration for an open source project–including the difficulties and the large benefits for adding continuous integration to an open source project.
#913
Sep 28, 2018 · 45 min
Prisma: GraphQL Infrastructure with Soren Bramer Schmidt
GraphQL allows developers to communicate with all of their different data backends through a consistent query interface. A GraphQL query can be translated into queries to MySQL, MongoDB, ElasticSearch, or whatever kind of API or backend is needed to fulfill the GraphQL query. GraphQL users need to set up a GraphQL server to fulfill this query federation. Prisma is a tool for automatically generating a GraphQL API and serving GraphQL queries. The developer defines a data model and deploys with Prisma. Prisma generates the necessary GraphQL infrastructure to serve queries from the developer’s database. This can allow the developer to get up and running faster than they would setting up GraphQL infrastructure and defining the middleware query layer by hand. Prisma is an open source project, but it is also a company. The opportunities to build a business around a GraphQL infrastructure layer are numerous. In recent episodes, we have explored the complexities of the “data platform.” From newer companies like Uber to older companies like Procter and Gamble, engineers are struggling to find and access their data sources. Data engineers and data scientists spend months configuring their infrastructure to connect to BI tools and run distributed queries. GraphQL could simplify data platforms by providing a unified, standardized layer. At this layer, you could also offer caching, virtual data sets, and crowdsourced queries from across the company. Soren Bramer Schmidt is the CTO and co-founder of Prisma, and he joins the show to discuss why GraphQL has become so popular, how Prisma works, and the opportunities to build developer tooling around GraphQL.
#911
Sep 27, 2018 · 58 min
Android Things with Wayne Piekarski
Internet of Things is a concept that describes lots of devices that you interact with regularly being connected to the Internet and networked together. Technologists have been dreaming of the world of IoT for many years, where our connected refrigerator can detect that we are out of food, and automatically order more food. Or our connected bathroom can scan us for diseases and recommend treatment. The bright future of IoT is slowly coming together. Hardware prototyping is getting cheaper. Voice interfaces and machine learning are creating new mediums for communicating with devices. Platforms like Kickstarter are allowing developers to validate the market for their products and raise the necessary capital to build their product. Android Things is a developer platform for IoT applications based on the Android Operating system. Android Things consists of hardware devices and software tools that reduce common IoT problems such as software updates and security patches. Wayne Piekarski is a staff developer advocate at Google, and he joins the show to talk about the state of IoT and why Google built Android Things.
#909
Sep 26, 2018 · 56 min
JavaScript Engines with Mathias Bynens
JavaScript performance has improved over time due to advances in JavaScript engines such as Google’s V8. A JavaScript engine performs compiler optimization, garbage collection, hot code management, caching, and other runtime aspects that keep a JavaScript program running efficiently. JavaScript runs in browsers and servers. The resources that are available to a JavaScript engine vary widely across different machines. JavaScript code is parsed into an abstract tree before being handed off to the compiler toolchain, in which one or more optimizing compilers produce efficient low-level code. In recent shows about WebAssembly, we have covered compiler pipelines. In an episode about GraalVM, we explored the impact that “code shape” has on the efficiency of JavaScript execution. Mathias Bynens is a developer advocate at Google working on the V8 JavaScript engine team. In today’s show we explore how a JavaScript engine works, and how compiler toolchains can adapt the hot code paths depending on what code needs to be optimized for.

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